Demographic Disparities in AI-Generated Versus Search-Engine-Sourced Images of Ophthalmologists: A Cross-Sectional Analysis

Vision (Basel). 2026 Feb 10;10(1):10. doi: 10.3390/vision10010010.

ABSTRACT

Purpose: To evaluate demographic representation in AI-generated and search-engine-sourced images of North American ophthalmologists, overall and stratified by subspecialty, and compare these with actual demographic data. Methods: This cross-sectional analysis examined 2000 images (1000 AI-generated and 1000 search-engine-sourced) across ten North American ophthalmology subspecialties. Images were sourced from four AI platforms (DALL·E 3, Firefly, Midjourney, Grok-2) and four search engines (Google, Bing, DuckDuckGo, Yahoo!). Using a standardized framework, reviewers assessed gender, race, age group, and professional attire. Pearson chi-squared tests were used to compare image sets with actual demographic data from the Association of American Medical Colleges and Canadian Institute for Health Information. Results: AI-generated images depicted 69% men compared to 64% in search-engine-sourced images (p = 0.047), though both were lower than the actual proportion of male ophthalmologists in North America (71-73%, p < 0.001). White individuals were overrepresented in AI-generated images (81%) relative to both search-engine-sourced images (74%, p = 0.001) and actual demographic data (69%, p < 0.001). Younger individuals (under 50 years) were significantly overrepresented in both image sets, with 82% in AI-generated images and 73% in search-engine-sourced images, compared to only 45-46% in actual demographic data (p < 0.001 for both). AI-generated images also depicted ophthalmologists with significantly more stereotypical medical accessories, including stethoscopes (17% vs. 2%, p < 0.001), glasses (45% vs. 30%, p < 0.001), and white coats (68% vs. 53%, p < 0.001), compared to search-engine-sourced images. Conclusions: AI-generated images diverge from actual demographics, presenting a younger, more stereotypical workforce that paradoxically aligns closer to gender parity than reality.

PMID:41718254 | PMC:PMC12921965 | DOI:10.3390/vision10010010